Overview

Dataset statistics

Number of variables24
Number of observations41703
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.0 MiB
Average record size in memory200.0 B

Variable types

Categorical12
Numeric11
DateTime1

Alerts

num_units has constant value ""Constant
project_name has a high cardinality: 1948 distinct valuesHigh cardinality
address has a high cardinality: 41672 distinct valuesHigh cardinality
nett_price has a high cardinality: 139 distinct valuesHigh cardinality
tenure has a high cardinality: 428 distinct valuesHigh cardinality
area_sqm is highly overall correlated with total_priceHigh correlation
total_price is highly overall correlated with area_sqm and 1 other fieldsHigh correlation
price_psm is highly overall correlated with price_psf and 4 other fieldsHigh correlation
price_psf is highly overall correlated with price_psm and 4 other fieldsHigh correlation
completion_date is highly overall correlated with sale_typeHigh correlation
postal_district is highly overall correlated with price_psm and 7 other fieldsHigh correlation
postal_sector is highly overall correlated with price_psm and 7 other fieldsHigh correlation
postal_code is highly overall correlated with price_psm and 7 other fieldsHigh correlation
lat is highly overall correlated with total_price and 7 other fieldsHigh correlation
lon is highly overall correlated with planning_region and 1 other fieldsHigh correlation
property_type is highly overall correlated with planning_areaHigh correlation
sale_type is highly overall correlated with completion_dateHigh correlation
planning_region is highly overall correlated with postal_district and 5 other fieldsHigh correlation
planning_area is highly overall correlated with postal_district and 7 other fieldsHigh correlation
is_leasehold is highly overall correlated with postal_district and 3 other fieldsHigh correlation
area_type is highly imbalanced (> 99.9%)Imbalance
nett_price is highly imbalanced (99.2%)Imbalance
address is uniformly distributedUniform

Reproduction

Analysis started2023-04-19 12:19:27.611798
Analysis finished2023-04-19 12:19:35.829458
Duration8.22 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

project_name
Categorical

Distinct1948
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size651.6 KiB
RIVERFRONT RESIDENCES
 
936
RIVERCOVE RESIDENCES
 
620
THE TAPESTRY
 
613
STIRLING RESIDENCES
 
598
PARK COLONIAL
 
579
Other values (1943)
38357 

Length

Max length47
Median length28
Mean length14.165288
Min length2

Characters and Unicode

Total characters590735
Distinct characters44
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique274 ?
Unique (%)0.7%

Sample

1st rowTHE BAYSHORE
2nd rowKINGSFORD WATERBAY
3rd rowTHE JOVELL
4th rowV ON SHENTON
5th rowTHE BEACON

Common Values

ValueCountFrequency (%)
RIVERFRONT RESIDENCES 936
 
2.2%
RIVERCOVE RESIDENCES 620
 
1.5%
THE TAPESTRY 613
 
1.5%
STIRLING RESIDENCES 598
 
1.4%
PARK COLONIAL 579
 
1.4%
PARC BOTANNIA 535
 
1.3%
HUNDRED PALMS RESIDENCES 531
 
1.3%
KINGSFORD WATERBAY 513
 
1.2%
PARC ESTA 483
 
1.2%
AFFINITY AT SERANGOON 468
 
1.1%
Other values (1938) 35827
85.9%

Length

2023-04-19T20:19:35.857006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
residences 7815
 
8.7%
the 7785
 
8.7%
park 2986
 
3.3%
parc 2264
 
2.5%
at 1973
 
2.2%
condominium 1062
 
1.2%
1025
 
1.1%
residence 993
 
1.1%
riverfront 936
 
1.0%
suites 795
 
0.9%
Other values (1600) 62023
69.2%

Most occurring characters

ValueCountFrequency (%)
E 81353
13.8%
R 49120
 
8.3%
47954
 
8.1%
A 45681
 
7.7%
S 45320
 
7.7%
I 42357
 
7.2%
N 40704
 
6.9%
T 35250
 
6.0%
O 26287
 
4.4%
C 22272
 
3.8%
Other values (34) 154437
26.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 537960
91.1%
Space Separator 47954
 
8.1%
Decimal Number 2799
 
0.5%
Other Punctuation 1908
 
0.3%
Dash Punctuation 114
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 81353
15.1%
R 49120
 
9.1%
A 45681
 
8.5%
S 45320
 
8.4%
I 42357
 
7.9%
N 40704
 
7.6%
T 35250
 
6.6%
O 26287
 
4.9%
C 22272
 
4.1%
L 20717
 
3.9%
Other values (16) 128899
24.0%
Decimal Number
ValueCountFrequency (%)
8 747
26.7%
1 402
14.4%
3 393
14.0%
2 347
12.4%
6 239
 
8.5%
5 236
 
8.4%
0 181
 
6.5%
4 146
 
5.2%
7 55
 
2.0%
9 53
 
1.9%
Other Punctuation
ValueCountFrequency (%)
@ 1292
67.7%
' 449
 
23.5%
. 132
 
6.9%
& 23
 
1.2%
# 8
 
0.4%
/ 4
 
0.2%
Space Separator
ValueCountFrequency (%)
47954
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 537960
91.1%
Common 52775
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 81353
15.1%
R 49120
 
9.1%
A 45681
 
8.5%
S 45320
 
8.4%
I 42357
 
7.9%
N 40704
 
7.6%
T 35250
 
6.6%
O 26287
 
4.9%
C 22272
 
4.1%
L 20717
 
3.9%
Other values (16) 128899
24.0%
Common
ValueCountFrequency (%)
47954
90.9%
@ 1292
 
2.4%
8 747
 
1.4%
' 449
 
0.9%
1 402
 
0.8%
3 393
 
0.7%
2 347
 
0.7%
6 239
 
0.5%
5 236
 
0.4%
0 181
 
0.3%
Other values (8) 535
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 590735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 81353
13.8%
R 49120
 
8.3%
47954
 
8.1%
A 45681
 
7.7%
S 45320
 
7.7%
I 42357
 
7.2%
N 40704
 
6.9%
T 35250
 
6.0%
O 26287
 
4.4%
C 22272
 
3.8%
Other values (34) 154437
26.1%

address
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct41672
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size651.6 KiB
489 Dunman Road #03-03
 
2
5 Jervois Close #07-10
 
2
70 Upper Serangoon View #07-33
 
2
376 Thomson Road #25-05
 
2
103 Prince Charles Crescent #09-07
 
2
Other values (41667)
41693 

Length

Max length42
Median length34
Mean length27.228761
Min length13

Characters and Unicode

Total characters1135521
Distinct characters66
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41641 ?
Unique (%)99.9%

Sample

1st row22 Bayshore Road #03-02
2nd row66 Upper Serangoon View #16-12
3rd row13 Flora Drive #02-11
4th row5A Shenton Way #44-12
5th row130 Cantonment Road #10-04

Common Values

ValueCountFrequency (%)
489 Dunman Road #03-03 2
 
< 0.1%
5 Jervois Close #07-10 2
 
< 0.1%
70 Upper Serangoon View #07-33 2
 
< 0.1%
376 Thomson Road #25-05 2
 
< 0.1%
103 Prince Charles Crescent #09-07 2
 
< 0.1%
10 Boon Lay Drive #03-30 2
 
< 0.1%
10 Martin Place #11-13 2
 
< 0.1%
336 River Valley Road #07-01 2
 
< 0.1%
4 Kovan Rise #08-09 2
 
< 0.1%
36 Dakota Crescent #14-05 2
 
< 0.1%
Other values (41662) 41683
> 99.9%

Length

2023-04-19T20:19:35.909932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
road 12726
 
6.6%
avenue 5670
 
2.9%
street 4292
 
2.2%
1 3184
 
1.6%
drive 2511
 
1.3%
3 1811
 
0.9%
7 1804
 
0.9%
serangoon 1771
 
0.9%
lane 1760
 
0.9%
coast 1718
 
0.9%
Other values (4238) 156067
80.7%

Most occurring characters

ValueCountFrequency (%)
234890
20.7%
e 63924
 
5.6%
a 57678
 
5.1%
0 56184
 
4.9%
1 53099
 
4.7%
o 45545
 
4.0%
n 42066
 
3.7%
- 41610
 
3.7%
# 41576
 
3.7%
2 32326
 
2.8%
Other values (56) 466623
41.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 452138
39.8%
Decimal Number 261712
23.0%
Space Separator 234890
20.7%
Uppercase Letter 103208
 
9.1%
Other Punctuation 41963
 
3.7%
Dash Punctuation 41610
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 63924
14.1%
a 57678
12.8%
o 45545
10.1%
n 42066
9.3%
r 32083
 
7.1%
i 28074
 
6.2%
t 25068
 
5.5%
d 19310
 
4.3%
l 19288
 
4.3%
s 19018
 
4.2%
Other values (16) 100084
22.1%
Uppercase Letter
ValueCountFrequency (%)
R 15905
15.4%
S 12154
11.8%
C 9916
 
9.6%
A 8592
 
8.3%
L 6388
 
6.2%
P 5613
 
5.4%
B 5096
 
4.9%
T 4868
 
4.7%
W 4448
 
4.3%
K 3727
 
3.6%
Other values (15) 26501
25.7%
Decimal Number
ValueCountFrequency (%)
0 56184
21.5%
1 53099
20.3%
2 32326
12.4%
3 26023
9.9%
5 19019
 
7.3%
6 17300
 
6.6%
4 17088
 
6.5%
8 14946
 
5.7%
7 14099
 
5.4%
9 11628
 
4.4%
Other Punctuation
ValueCountFrequency (%)
# 41576
99.1%
. 242
 
0.6%
' 145
 
0.3%
Space Separator
ValueCountFrequency (%)
234890
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41610
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 580175
51.1%
Latin 555346
48.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 63924
 
11.5%
a 57678
 
10.4%
o 45545
 
8.2%
n 42066
 
7.6%
r 32083
 
5.8%
i 28074
 
5.1%
t 25068
 
4.5%
d 19310
 
3.5%
l 19288
 
3.5%
s 19018
 
3.4%
Other values (41) 203292
36.6%
Common
ValueCountFrequency (%)
234890
40.5%
0 56184
 
9.7%
1 53099
 
9.2%
- 41610
 
7.2%
# 41576
 
7.2%
2 32326
 
5.6%
3 26023
 
4.5%
5 19019
 
3.3%
6 17300
 
3.0%
4 17088
 
2.9%
Other values (5) 41060
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1135521
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
234890
20.7%
e 63924
 
5.6%
a 57678
 
5.1%
0 56184
 
4.9%
1 53099
 
4.7%
o 45545
 
4.0%
n 42066
 
3.7%
- 41610
 
3.7%
# 41576
 
3.7%
2 32326
 
2.8%
Other values (56) 466623
41.1%

num_units
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size651.6 KiB
1
41703 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41703
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 41703
100.0%

Length

2023-04-19T20:19:35.954819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T20:19:35.997179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 41703
100.0%

Most occurring characters

ValueCountFrequency (%)
1 41703
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41703
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 41703
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 41703
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 41703
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41703
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 41703
100.0%

area_sqm
Real number (ℝ)

Distinct432
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.724193
Minimum24
Maximum898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size651.6 KiB
2023-04-19T20:19:36.040504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile43
Q167
median93
Q3117
95-th percentile181
Maximum898
Range874
Interquartile range (IQR)50

Descriptive statistics

Standard deviation50.723676
Coefficient of variation (CV)0.50863962
Kurtosis21.104631
Mean99.724193
Median Absolute Deviation (MAD)25
Skewness3.1329406
Sum4158798
Variance2572.8913
MonotonicityNot monotonic
2023-04-19T20:19:36.093286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 709
 
1.7%
103 680
 
1.6%
89 668
 
1.6%
91 622
 
1.5%
85 592
 
1.4%
84 589
 
1.4%
99 579
 
1.4%
98 554
 
1.3%
65 548
 
1.3%
57 543
 
1.3%
Other values (422) 35619
85.4%
ValueCountFrequency (%)
24 1
 
< 0.1%
30 3
 
< 0.1%
31 12
 
< 0.1%
32 23
 
0.1%
33 33
 
0.1%
34 68
0.2%
35 39
0.1%
36 54
0.1%
37 96
0.2%
38 78
0.2%
ValueCountFrequency (%)
898 1
< 0.1%
864 1
< 0.1%
742 1
< 0.1%
728 1
< 0.1%
717 1
< 0.1%
697 1
< 0.1%
677 2
< 0.1%
657 1
< 0.1%
656 1
< 0.1%
655 1
< 0.1%

area_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size651.6 KiB
Strata
41702 
Land
 
1

Length

Max length6
Median length6
Mean length5.999952
Min length4

Characters and Unicode

Total characters250216
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowStrata
2nd rowStrata
3rd rowStrata
4th rowStrata
5th rowStrata

Common Values

ValueCountFrequency (%)
Strata 41702
> 99.9%
Land 1
 
< 0.1%

Length

2023-04-19T20:19:36.141132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T20:19:36.189720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
strata 41702
> 99.9%
land 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 83405
33.3%
t 83404
33.3%
S 41702
16.7%
r 41702
16.7%
L 1
 
< 0.1%
n 1
 
< 0.1%
d 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 208513
83.3%
Uppercase Letter 41703
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 83405
40.0%
t 83404
40.0%
r 41702
20.0%
n 1
 
< 0.1%
d 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
S 41702
> 99.9%
L 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 250216
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 83405
33.3%
t 83404
33.3%
S 41702
16.7%
r 41702
16.7%
L 1
 
< 0.1%
n 1
 
< 0.1%
d 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 250216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 83405
33.3%
t 83404
33.3%
S 41702
16.7%
r 41702
16.7%
L 1
 
< 0.1%
n 1
 
< 0.1%
d 1
 
< 0.1%

total_price
Real number (ℝ)

Distinct9151
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1467677.4
Minimum358000
Maximum36280000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size651.6 KiB
2023-04-19T20:19:36.234435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum358000
5-th percentile668530
Q1903000
median1190000
Q31626719.5
95-th percentile3067814
Maximum36280000
Range35922000
Interquartile range (IQR)723719.5

Descriptive statistics

Standard deviation1139893.6
Coefficient of variation (CV)0.77666493
Kurtosis82.524626
Mean1467677.4
Median Absolute Deviation (MAD)330000
Skewness6.4045092
Sum6.1206552 × 1010
Variance1.2993574 × 1012
MonotonicityNot monotonic
2023-04-19T20:19:36.290567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200000 306
 
0.7%
1100000 301
 
0.7%
1300000 262
 
0.6%
1050000 258
 
0.6%
1150000 252
 
0.6%
1500000 226
 
0.5%
1400000 218
 
0.5%
1350000 211
 
0.5%
1180000 205
 
0.5%
1250000 205
 
0.5%
Other values (9141) 39259
94.1%
ValueCountFrequency (%)
358000 1
< 0.1%
362000 1
< 0.1%
369000 1
< 0.1%
371000 1
< 0.1%
373000 1
< 0.1%
376000 2
< 0.1%
380000 1
< 0.1%
383000 1
< 0.1%
388000 1
< 0.1%
392000 2
< 0.1%
ValueCountFrequency (%)
36280000 1
< 0.1%
28000000 1
< 0.1%
26000000 1
< 0.1%
25575000 1
< 0.1%
24500000 1
< 0.1%
24000000 1
< 0.1%
21861000 1
< 0.1%
20800000 1
< 0.1%
19600000 1
< 0.1%
18270240 1
< 0.1%

nett_price
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct139
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size651.6 KiB
-
41558 
895000
 
2
889000
 
2
853000
 
2
802700
 
2
Other values (134)
 
137

Length

Max length7
Median length1
Mean length1.0191113
Min length1

Characters and Unicode

Total characters42500
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique131 ?
Unique (%)0.3%

Sample

1st row-
2nd row-
3rd row-
4th row2847680
5th row-

Common Values

ValueCountFrequency (%)
- 41558
99.7%
895000 2
 
< 0.1%
889000 2
 
< 0.1%
853000 2
 
< 0.1%
802700 2
 
< 0.1%
1296000 2
 
< 0.1%
887000 2
 
< 0.1%
868420 2
 
< 0.1%
2927161 1
 
< 0.1%
995712 1
 
< 0.1%
Other values (129) 129
 
0.3%

Length

2023-04-19T20:19:36.342047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
41558
99.7%
889000 2
 
< 0.1%
853000 2
 
< 0.1%
802700 2
 
< 0.1%
1296000 2
 
< 0.1%
887000 2
 
< 0.1%
868420 2
 
< 0.1%
895000 2
 
< 0.1%
825700 1
 
< 0.1%
856000 1
 
< 0.1%
Other values (129) 129
 
0.3%

Most occurring characters

ValueCountFrequency (%)
- 41558
97.8%
0 329
 
0.8%
1 118
 
0.3%
8 88
 
0.2%
4 69
 
0.2%
9 67
 
0.2%
7 63
 
0.1%
2 58
 
0.1%
3 53
 
0.1%
5 51
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 41558
97.8%
Decimal Number 942
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 329
34.9%
1 118
 
12.5%
8 88
 
9.3%
4 69
 
7.3%
9 67
 
7.1%
7 63
 
6.7%
2 58
 
6.2%
3 53
 
5.6%
5 51
 
5.4%
6 46
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
- 41558
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 42500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 41558
97.8%
0 329
 
0.8%
1 118
 
0.3%
8 88
 
0.2%
4 69
 
0.2%
9 67
 
0.2%
7 63
 
0.1%
2 58
 
0.1%
3 53
 
0.1%
5 51
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 41558
97.8%
0 329
 
0.8%
1 118
 
0.3%
8 88
 
0.2%
4 69
 
0.2%
9 67
 
0.2%
7 63
 
0.1%
2 58
 
0.1%
3 53
 
0.1%
5 51
 
0.1%

price_psm
Real number (ℝ)

Distinct14851
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14937.68
Minimum4762
Maximum53030
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size651.6 KiB
2023-04-19T20:19:36.390560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4762
5-th percentile8116
Q110848.5
median14338
Q317813.5
95-th percentile24754.4
Maximum53030
Range48268
Interquartile range (IQR)6965

Descriptive statistics

Standard deviation5451.919
Coefficient of variation (CV)0.36497764
Kurtosis2.8492157
Mean14937.68
Median Absolute Deviation (MAD)3484
Skewness1.2749083
Sum6.2294605 × 108
Variance29723421
MonotonicityNot monotonic
2023-04-19T20:19:36.446771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 130
 
0.3%
15000 84
 
0.2%
16667 73
 
0.2%
12500 73
 
0.2%
20000 67
 
0.2%
14000 56
 
0.1%
13333 56
 
0.1%
14286 56
 
0.1%
16000 48
 
0.1%
18000 48
 
0.1%
Other values (14841) 41012
98.3%
ValueCountFrequency (%)
4762 1
< 0.1%
4875 1
< 0.1%
5251 1
< 0.1%
5275 1
< 0.1%
5325 1
< 0.1%
5353 1
< 0.1%
5357 1
< 0.1%
5358 1
< 0.1%
5392 1
< 0.1%
5414 1
< 0.1%
ValueCountFrequency (%)
53030 1
< 0.1%
49835 1
< 0.1%
49080 1
< 0.1%
46667 1
< 0.1%
46281 1
< 0.1%
44984 1
< 0.1%
44940 1
< 0.1%
44892 1
< 0.1%
44113 1
< 0.1%
43919 1
< 0.1%

price_psf
Real number (ℝ)

Distinct2748
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1387.7441
Minimum442
Maximum4927
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size651.6 KiB
2023-04-19T20:19:36.502131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum442
5-th percentile754
Q11008
median1332
Q31655
95-th percentile2299.9
Maximum4927
Range4485
Interquartile range (IQR)647

Descriptive statistics

Standard deviation506.49361
Coefficient of variation (CV)0.36497622
Kurtosis2.8493611
Mean1387.7441
Median Absolute Deviation (MAD)324
Skewness1.274937
Sum57873093
Variance256535.77
MonotonicityNot monotonic
2023-04-19T20:19:36.554388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
929 132
 
0.3%
1548 96
 
0.2%
1394 89
 
0.2%
1327 79
 
0.2%
1161 76
 
0.2%
1858 70
 
0.2%
1301 69
 
0.2%
1032 66
 
0.2%
1239 65
 
0.2%
1340 64
 
0.2%
Other values (2738) 40897
98.1%
ValueCountFrequency (%)
442 1
< 0.1%
453 1
< 0.1%
488 1
< 0.1%
490 1
< 0.1%
495 1
< 0.1%
497 1
< 0.1%
498 2
< 0.1%
501 1
< 0.1%
503 1
< 0.1%
506 2
< 0.1%
ValueCountFrequency (%)
4927 1
< 0.1%
4630 1
< 0.1%
4560 1
< 0.1%
4335 1
< 0.1%
4300 1
< 0.1%
4179 1
< 0.1%
4175 1
< 0.1%
4171 1
< 0.1%
4098 1
< 0.1%
4080 1
< 0.1%
Distinct698
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size651.6 KiB
Minimum2017-05-01 00:00:00
Maximum2019-03-31 00:00:00
2023-04-19T20:19:36.608548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:36.660815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

property_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size651.6 KiB
Condominium
22201 
Apartment
14940 
Executive Condominium
4562 

Length

Max length21
Median length11
Mean length11.377431
Min length9

Characters and Unicode

Total characters474473
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCondominium
2nd rowApartment
3rd rowCondominium
4th rowApartment
5th rowApartment

Common Values

ValueCountFrequency (%)
Condominium 22201
53.2%
Apartment 14940
35.8%
Executive Condominium 4562
 
10.9%

Length

2023-04-19T20:19:36.710742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T20:19:36.762285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
condominium 26763
57.8%
apartment 14940
32.3%
executive 4562
 
9.9%

Most occurring characters

ValueCountFrequency (%)
n 68466
14.4%
m 68466
14.4%
i 58088
12.2%
o 53526
11.3%
t 34442
7.3%
u 31325
6.6%
C 26763
 
5.6%
d 26763
 
5.6%
e 24064
 
5.1%
r 14940
 
3.1%
Other values (8) 67630
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 423646
89.3%
Uppercase Letter 46265
 
9.8%
Space Separator 4562
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 68466
16.2%
m 68466
16.2%
i 58088
13.7%
o 53526
12.6%
t 34442
8.1%
u 31325
7.4%
d 26763
 
6.3%
e 24064
 
5.7%
r 14940
 
3.5%
a 14940
 
3.5%
Other values (4) 28626
6.8%
Uppercase Letter
ValueCountFrequency (%)
C 26763
57.8%
A 14940
32.3%
E 4562
 
9.9%
Space Separator
ValueCountFrequency (%)
4562
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 469911
99.0%
Common 4562
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 68466
14.6%
m 68466
14.6%
i 58088
12.4%
o 53526
11.4%
t 34442
7.3%
u 31325
6.7%
C 26763
 
5.7%
d 26763
 
5.7%
e 24064
 
5.1%
r 14940
 
3.2%
Other values (7) 63068
13.4%
Common
ValueCountFrequency (%)
4562
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 474473
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 68466
14.4%
m 68466
14.4%
i 58088
12.2%
o 53526
11.3%
t 34442
7.3%
u 31325
6.6%
C 26763
 
5.6%
d 26763
 
5.6%
e 24064
 
5.1%
r 14940
 
3.1%
Other values (8) 67630
14.3%

tenure
Categorical

Distinct428
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size651.6 KiB
Freehold
11073 
99 Yrs From 31/05/2018
 
936
99 Yrs From 05/12/2016
 
620
99 Yrs From 31/07/2017
 
613
99 Yrs From 25/08/2014
 
599
Other values (423)
27862 

Length

Max length24
Median length22
Mean length18.284776
Min length4

Characters and Unicode

Total characters762530
Distinct characters27
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)< 0.1%

Sample

1st row99 Yrs From 31/05/1993
2nd row99 Yrs From 03/03/2014
3rd row99 Yrs From 08/03/2018
4th row99 Yrs From 29/11/2011
5th row99 Yrs From 05/03/2004

Common Values

ValueCountFrequency (%)
Freehold 11073
26.6%
99 Yrs From 31/05/2018 936
 
2.2%
99 Yrs From 05/12/2016 620
 
1.5%
99 Yrs From 31/07/2017 613
 
1.5%
99 Yrs From 25/08/2014 599
 
1.4%
99 Yrs From 18/08/2017 598
 
1.4%
99 Yrs From 11/10/2017 579
 
1.4%
99 Yrs From 28/12/2016 535
 
1.3%
99 Yrs From 30/05/2016 531
 
1.3%
99 Yrs From 03/03/2014 513
 
1.2%
Other values (418) 25106
60.2%

Length

2023-04-19T20:19:36.811134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
from 30333
22.8%
yrs 30333
22.8%
99 29363
22.0%
freehold 11073
 
8.3%
999 988
 
0.7%
31/05/2018 936
 
0.7%
05/12/2016 620
 
0.5%
31/07/2017 613
 
0.5%
25/08/2014 599
 
0.4%
18/08/2017 598
 
0.4%
Other values (431) 27836
20.9%

Most occurring characters

ValueCountFrequency (%)
91623
12.0%
9 75429
9.9%
r 72034
 
9.4%
0 67676
 
8.9%
/ 60666
 
8.0%
1 55089
 
7.2%
2 43797
 
5.7%
o 41701
 
5.5%
F 41406
 
5.4%
s 30923
 
4.1%
Other values (17) 182186
23.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 305188
40.0%
Lowercase Letter 232716
30.5%
Space Separator 91623
 
12.0%
Uppercase Letter 72333
 
9.5%
Other Punctuation 60670
 
8.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 75429
24.7%
0 67676
22.2%
1 55089
18.1%
2 43797
14.4%
8 13577
 
4.4%
5 12235
 
4.0%
7 11006
 
3.6%
3 10171
 
3.3%
6 9337
 
3.1%
4 6871
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
r 72034
31.0%
o 41701
17.9%
s 30923
13.3%
m 30333
13.0%
e 23031
 
9.9%
l 11368
 
4.9%
h 11368
 
4.9%
d 11368
 
4.9%
a 590
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
F 41406
57.2%
Y 30628
42.3%
L 295
 
0.4%
N 2
 
< 0.1%
A 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 60666
> 99.9%
. 4
 
< 0.1%
Space Separator
ValueCountFrequency (%)
91623
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 457481
60.0%
Latin 305049
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 72034
23.6%
o 41701
13.7%
F 41406
13.6%
s 30923
10.1%
Y 30628
10.0%
m 30333
9.9%
e 23031
 
7.5%
l 11368
 
3.7%
h 11368
 
3.7%
d 11368
 
3.7%
Other values (4) 889
 
0.3%
Common
ValueCountFrequency (%)
91623
20.0%
9 75429
16.5%
0 67676
14.8%
/ 60666
13.3%
1 55089
12.0%
2 43797
9.6%
8 13577
 
3.0%
5 12235
 
2.7%
7 11006
 
2.4%
3 10171
 
2.2%
Other values (3) 16212
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 762530
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
91623
12.0%
9 75429
9.9%
r 72034
 
9.4%
0 67676
 
8.9%
/ 60666
 
8.0%
1 55089
 
7.2%
2 43797
 
5.7%
o 41701
 
5.5%
F 41406
 
5.4%
s 30923
 
4.1%
Other values (17) 182186
23.9%

completion_date
Real number (ℝ)

Distinct56
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.8654
Minimum1953
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size651.6 KiB
2023-04-19T20:19:36.866092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1953
5-th percentile1996
Q12008
median2016
Q32023
95-th percentile2023
Maximum2023
Range70
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.134357
Coefficient of variation (CV)0.0050322909
Kurtosis0.92124435
Mean2013.8654
Median Absolute Deviation (MAD)7
Skewness-1.1134015
Sum83984229
Variance102.70518
MonotonicityNot monotonic
2023-04-19T20:19:36.922513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2023 16839
40.4%
2017 2558
 
6.1%
2015 1960
 
4.7%
2014 1936
 
4.6%
2016 1763
 
4.2%
2013 1396
 
3.3%
2011 1183
 
2.8%
2010 954
 
2.3%
2012 951
 
2.3%
2009 946
 
2.3%
Other values (46) 11217
26.9%
ValueCountFrequency (%)
1953 2
 
< 0.1%
1954 2
 
< 0.1%
1955 1
 
< 0.1%
1966 1
 
< 0.1%
1967 2
 
< 0.1%
1968 1
 
< 0.1%
1971 2
 
< 0.1%
1972 11
< 0.1%
1973 1
 
< 0.1%
1974 7
< 0.1%
ValueCountFrequency (%)
2023 16839
40.4%
2019 17
 
< 0.1%
2018 483
 
1.2%
2017 2558
 
6.1%
2016 1763
 
4.2%
2015 1960
 
4.7%
2014 1936
 
4.6%
2013 1396
 
3.3%
2012 951
 
2.3%
2011 1183
 
2.8%

sale_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size651.6 KiB
Resale
22019 
New Sale
19070 
Sub Sale
 
614

Length

Max length8
Median length6
Mean length6.9440088
Min length6

Characters and Unicode

Total characters289586
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResale
2nd rowSub Sale
3rd rowNew Sale
4th rowNew Sale
5th rowResale

Common Values

ValueCountFrequency (%)
Resale 22019
52.8%
New Sale 19070
45.7%
Sub Sale 614
 
1.5%

Length

2023-04-19T20:19:36.978121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T20:19:37.029002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
resale 22019
35.9%
sale 19684
32.1%
new 19070
31.1%
sub 614
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 82792
28.6%
a 41703
14.4%
l 41703
14.4%
R 22019
 
7.6%
s 22019
 
7.6%
S 20298
 
7.0%
19684
 
6.8%
N 19070
 
6.6%
w 19070
 
6.6%
u 614
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 208515
72.0%
Uppercase Letter 61387
 
21.2%
Space Separator 19684
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 82792
39.7%
a 41703
20.0%
l 41703
20.0%
s 22019
 
10.6%
w 19070
 
9.1%
u 614
 
0.3%
b 614
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
R 22019
35.9%
S 20298
33.1%
N 19070
31.1%
Space Separator
ValueCountFrequency (%)
19684
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 269902
93.2%
Common 19684
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 82792
30.7%
a 41703
15.5%
l 41703
15.5%
R 22019
 
8.2%
s 22019
 
8.2%
S 20298
 
7.5%
N 19070
 
7.1%
w 19070
 
7.1%
u 614
 
0.2%
b 614
 
0.2%
Common
ValueCountFrequency (%)
19684
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 289586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 82792
28.6%
a 41703
14.4%
l 41703
14.4%
R 22019
 
7.6%
s 22019
 
7.6%
S 20298
 
7.0%
19684
 
6.8%
N 19070
 
6.6%
w 19070
 
6.6%
u 614
 
0.2%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size651.6 KiB
Private
19700 
HDB
15796 
N.A
6207 

Length

Max length7
Median length3
Mean length4.8895523
Min length3

Characters and Unicode

Total characters203909
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate
2nd rowPrivate
3rd rowN.A
4th rowN.A
5th rowPrivate

Common Values

ValueCountFrequency (%)
Private 19700
47.2%
HDB 15796
37.9%
N.A 6207
 
14.9%

Length

2023-04-19T20:19:37.072924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T20:19:37.125729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
private 19700
47.2%
hdb 15796
37.9%
n.a 6207
 
14.9%

Most occurring characters

ValueCountFrequency (%)
P 19700
9.7%
r 19700
9.7%
i 19700
9.7%
v 19700
9.7%
a 19700
9.7%
t 19700
9.7%
e 19700
9.7%
H 15796
7.7%
D 15796
7.7%
B 15796
7.7%
Other values (3) 18621
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 118200
58.0%
Uppercase Letter 79502
39.0%
Other Punctuation 6207
 
3.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 19700
24.8%
H 15796
19.9%
D 15796
19.9%
B 15796
19.9%
N 6207
 
7.8%
A 6207
 
7.8%
Lowercase Letter
ValueCountFrequency (%)
r 19700
16.7%
i 19700
16.7%
v 19700
16.7%
a 19700
16.7%
t 19700
16.7%
e 19700
16.7%
Other Punctuation
ValueCountFrequency (%)
. 6207
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 197702
97.0%
Common 6207
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 19700
10.0%
r 19700
10.0%
i 19700
10.0%
v 19700
10.0%
a 19700
10.0%
t 19700
10.0%
e 19700
10.0%
H 15796
8.0%
D 15796
8.0%
B 15796
8.0%
Other values (2) 12414
6.3%
Common
ValueCountFrequency (%)
. 6207
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203909
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 19700
9.7%
r 19700
9.7%
i 19700
9.7%
v 19700
9.7%
a 19700
9.7%
t 19700
9.7%
e 19700
9.7%
H 15796
7.7%
D 15796
7.7%
B 15796
7.7%
Other values (3) 18621
9.1%

postal_district
Real number (ℝ)

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.982231
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size651.6 KiB
2023-04-19T20:19:37.168378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110
median15
Q319
95-th percentile27
Maximum28
Range27
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.0195228
Coefficient of variation (CV)0.46852318
Kurtosis-0.7830344
Mean14.982231
Median Absolute Deviation (MAD)5
Skewness-0.15634662
Sum624804
Variance49.2737
MonotonicityNot monotonic
2023-04-19T20:19:37.216607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
19 6155
14.8%
15 2817
 
6.8%
5 2795
 
6.7%
18 2759
 
6.6%
3 2696
 
6.5%
14 2524
 
6.1%
23 2485
 
6.0%
27 2281
 
5.5%
9 2258
 
5.4%
10 2130
 
5.1%
Other values (17) 12803
30.7%
ValueCountFrequency (%)
1 426
 
1.0%
2 459
 
1.1%
3 2696
6.5%
4 518
 
1.2%
5 2795
6.7%
6 2
 
< 0.1%
7 223
 
0.5%
8 505
 
1.2%
9 2258
5.4%
10 2130
5.1%
ValueCountFrequency (%)
28 835
 
2.0%
27 2281
 
5.5%
26 251
 
0.6%
25 617
 
1.5%
23 2485
6.0%
22 728
 
1.7%
21 1157
 
2.8%
20 1107
 
2.7%
19 6155
14.8%
18 2759
6.6%

postal_sector
Real number (ℝ)

Distinct69
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.709086
Minimum1
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size651.6 KiB
2023-04-19T20:19:37.270112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q124
median44
Q355
95-th percentile76
Maximum82
Range81
Interquartile range (IQR)31

Descriptive statistics

Standard deviation20.530996
Coefficient of variation (CV)0.4807173
Kurtosis-0.95584916
Mean42.709086
Median Absolute Deviation (MAD)14
Skewness-0.034008572
Sum1781097
Variance421.52181
MonotonicityNot monotonic
2023-04-19T20:19:37.326795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 2651
 
6.4%
54 2179
 
5.2%
12 1965
 
4.7%
52 1912
 
4.6%
76 1760
 
4.2%
14 1417
 
3.4%
23 1351
 
3.2%
35 1325
 
3.2%
43 1264
 
3.0%
15 988
 
2.4%
Other values (59) 24891
59.7%
ValueCountFrequency (%)
1 289
 
0.7%
5 13
 
< 0.1%
6 124
 
0.3%
7 321
 
0.8%
8 138
 
0.3%
9 390
 
0.9%
10 128
 
0.3%
11 681
 
1.6%
12 1965
4.7%
13 149
 
0.4%
ValueCountFrequency (%)
82 531
 
1.3%
80 125
 
0.3%
79 710
1.7%
78 231
 
0.6%
77 20
 
< 0.1%
76 1760
4.2%
75 521
 
1.2%
73 617
 
1.5%
68 941
2.3%
67 490
 
1.2%

postal_code
Real number (ℝ)

Distinct4647
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean434943.53
Minimum18965
Maximum828843
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size651.6 KiB
2023-04-19T20:19:37.386650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum18965
5-th percentile126751
Q1249566
median449028
Q3554341
95-th percentile768131.9
Maximum828843
Range809878
Interquartile range (IQR)304775

Descriptive statistics

Standard deviation204878.16
Coefficient of variation (CV)0.47104543
Kurtosis-0.94666983
Mean434943.53
Median Absolute Deviation (MAD)141071
Skewness-0.028736184
Sum1.813845 × 1010
Variance4.1975062 × 1010
MonotonicityNot monotonic
2023-04-19T20:19:37.442871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126756 296
 
0.7%
148960 294
 
0.7%
148962 278
 
0.7%
158745 203
 
0.5%
149456 185
 
0.4%
237963 180
 
0.4%
357688 174
 
0.4%
149457 168
 
0.4%
533813 167
 
0.4%
126754 167
 
0.4%
Other values (4637) 39591
94.9%
ValueCountFrequency (%)
18965 3
 
< 0.1%
18978 107
0.3%
18979 82
0.2%
18980 25
 
0.1%
18985 32
 
0.1%
18987 40
 
0.1%
58416 2
 
< 0.1%
59108 11
 
< 0.1%
68803 37
 
0.1%
68814 42
 
0.1%
ValueCountFrequency (%)
828843 2
 
< 0.1%
828842 6
< 0.1%
828841 1
 
< 0.1%
828840 1
 
< 0.1%
828833 6
< 0.1%
828832 10
< 0.1%
828831 7
< 0.1%
828830 5
< 0.1%
828823 7
< 0.1%
828822 10
< 0.1%

planning_region
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size651.6 KiB
Central Region
19402 
North East Region
7420 
East Region
6405 
West Region
5526 
North Region
2950 

Length

Max length17
Median length14
Mean length13.534014
Min length11

Characters and Unicode

Total characters564409
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEast Region
2nd rowNorth East Region
3rd rowEast Region
4th rowCentral Region
5th rowCentral Region

Common Values

ValueCountFrequency (%)
Central Region 19402
46.5%
North East Region 7420
 
17.8%
East Region 6405
 
15.4%
West Region 5526
 
13.3%
North Region 2950
 
7.1%

Length

2023-04-19T20:19:37.496937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T20:19:37.548469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
region 41703
45.9%
central 19402
21.4%
east 13825
 
15.2%
north 10370
 
11.4%
west 5526
 
6.1%

Most occurring characters

ValueCountFrequency (%)
e 66631
11.8%
n 61105
10.8%
o 52073
9.2%
t 49123
8.7%
49123
8.7%
R 41703
7.4%
i 41703
7.4%
g 41703
7.4%
a 33227
 
5.9%
r 29772
 
5.3%
Other values (7) 98246
17.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 424460
75.2%
Uppercase Letter 90826
 
16.1%
Space Separator 49123
 
8.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 66631
15.7%
n 61105
14.4%
o 52073
12.3%
t 49123
11.6%
i 41703
9.8%
g 41703
9.8%
a 33227
7.8%
r 29772
7.0%
l 19402
 
4.6%
s 19351
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
R 41703
45.9%
C 19402
21.4%
E 13825
 
15.2%
N 10370
 
11.4%
W 5526
 
6.1%
Space Separator
ValueCountFrequency (%)
49123
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 515286
91.3%
Common 49123
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 66631
12.9%
n 61105
11.9%
o 52073
10.1%
t 49123
9.5%
R 41703
8.1%
i 41703
8.1%
g 41703
8.1%
a 33227
6.4%
r 29772
 
5.8%
C 19402
 
3.8%
Other values (6) 78844
15.3%
Common
ValueCountFrequency (%)
49123
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 564409
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 66631
11.8%
n 61105
10.8%
o 52073
9.2%
t 49123
8.7%
49123
8.7%
R 41703
7.4%
i 41703
7.4%
g 41703
7.4%
a 33227
 
5.9%
r 29772
 
5.3%
Other values (7) 98246
17.4%

planning_area
Categorical

Distinct37
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size651.6 KiB
Hougang
3405 
Bedok
2881 
Geylang
 
2439
Queenstown
 
2322
Clementi
 
2018
Other values (32)
28638 

Length

Max length16
Median length13
Mean length8.577968
Min length5

Characters and Unicode

Total characters357727
Distinct characters44
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBedok
2nd rowHougang
3rd rowPasir Ris
4th rowDowntown Core
5th rowBukit Merah

Common Values

ValueCountFrequency (%)
Hougang 3405
 
8.2%
Bedok 2881
 
6.9%
Geylang 2439
 
5.8%
Queenstown 2322
 
5.6%
Clementi 2018
 
4.8%
Tampines 2003
 
4.8%
Toa Payoh 1855
 
4.4%
Sengkang 1847
 
4.4%
Bukit Merah 1716
 
4.1%
Bukit Timah 1702
 
4.1%
Other values (27) 19515
46.8%

Length

2023-04-19T20:19:37.597430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bukit 5235
 
9.2%
hougang 3405
 
6.0%
bedok 2881
 
5.0%
geylang 2439
 
4.3%
queenstown 2322
 
4.1%
clementi 2018
 
3.5%
tampines 2003
 
3.5%
toa 1855
 
3.3%
payoh 1855
 
3.3%
sengkang 1847
 
3.2%
Other values (39) 31197
54.7%

Most occurring characters

ValueCountFrequency (%)
a 37304
 
10.4%
n 36745
 
10.3%
e 29969
 
8.4%
o 25957
 
7.3%
g 21503
 
6.0%
i 20468
 
5.7%
15354
 
4.3%
u 14913
 
4.2%
t 13026
 
3.6%
l 11990
 
3.4%
Other values (34) 130498
36.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 285316
79.8%
Uppercase Letter 57057
 
15.9%
Space Separator 15354
 
4.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 37304
13.1%
n 36745
12.9%
e 29969
10.5%
o 25957
9.1%
g 21503
 
7.5%
i 20468
 
7.2%
u 14913
 
5.2%
t 13026
 
4.6%
l 11990
 
4.2%
s 11306
 
4.0%
Other values (12) 62135
21.8%
Uppercase Letter
ValueCountFrequency (%)
B 10274
18.0%
T 6567
11.5%
P 5442
9.5%
C 4768
8.4%
S 4305
7.5%
R 3461
 
6.1%
H 3405
 
6.0%
M 3176
 
5.6%
K 2804
 
4.9%
G 2439
 
4.3%
Other values (11) 10416
18.3%
Space Separator
ValueCountFrequency (%)
15354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 342373
95.7%
Common 15354
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 37304
 
10.9%
n 36745
 
10.7%
e 29969
 
8.8%
o 25957
 
7.6%
g 21503
 
6.3%
i 20468
 
6.0%
u 14913
 
4.4%
t 13026
 
3.8%
l 11990
 
3.5%
s 11306
 
3.3%
Other values (33) 119192
34.8%
Common
ValueCountFrequency (%)
15354
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 357727
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 37304
 
10.4%
n 36745
 
10.3%
e 29969
 
8.4%
o 25957
 
7.3%
g 21503
 
6.0%
i 20468
 
5.7%
15354
 
4.3%
u 14913
 
4.2%
t 13026
 
3.6%
l 11990
 
3.4%
Other values (34) 130498
36.5%

date_id
Real number (ℝ)

Distinct698
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean306.83512
Minimum0
Maximum697
Zeros67
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size651.6 KiB
2023-04-19T20:19:37.646017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28
Q1136
median310
Q3437
95-th percentile654
Maximum697
Range697
Interquartile range (IQR)301

Descriptive statistics

Standard deviation191.93928
Coefficient of variation (CV)0.62554533
Kurtosis-0.97132853
Mean306.83512
Median Absolute Deviation (MAD)153
Skewness0.24317778
Sum12795945
Variance36840.686
MonotonicityNot monotonic
2023-04-19T20:19:37.699236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
429 990
 
2.4%
82 590
 
1.4%
347 465
 
1.1%
368 375
 
0.9%
508 308
 
0.7%
96 305
 
0.7%
326 304
 
0.7%
689 249
 
0.6%
194 235
 
0.6%
340 228
 
0.5%
Other values (688) 37654
90.3%
ValueCountFrequency (%)
0 67
0.2%
1 81
0.2%
2 96
0.2%
3 71
0.2%
4 105
0.3%
5 38
 
0.1%
6 47
 
0.1%
7 123
0.3%
8 82
0.2%
9 62
0.1%
ValueCountFrequency (%)
697 41
 
0.1%
696 41
 
0.1%
695 39
 
0.1%
694 55
 
0.1%
693 49
 
0.1%
692 48
 
0.1%
691 57
 
0.1%
690 38
 
0.1%
689 249
0.6%
688 74
 
0.2%

is_leasehold
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size651.6 KiB
1
29366 
0
12337 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41703
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 29366
70.4%
0 12337
29.6%

Length

2023-04-19T20:19:37.749461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-19T20:19:37.794062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 29366
70.4%
0 12337
29.6%

Most occurring characters

ValueCountFrequency (%)
1 29366
70.4%
0 12337
29.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41703
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 29366
70.4%
0 12337
29.6%

Most occurring scripts

ValueCountFrequency (%)
Common 41703
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 29366
70.4%
0 12337
29.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41703
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 29366
70.4%
0 12337
29.6%

lat
Real number (ℝ)

Distinct4540
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3396511
Minimum1.2393366
Maximum1.4564363
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size651.6 KiB
2023-04-19T20:19:37.841018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.2393366
5-th percentile1.2845173
Q11.3078028
median1.333824
Q31.3692643
95-th percentile1.4239064
Maximum1.4564363
Range0.21709963
Interquartile range (IQR)0.06146149

Descriptive statistics

Standard deviation0.041217093
Coefficient of variation (CV)0.030767036
Kurtosis-0.11519856
Mean1.3396511
Median Absolute Deviation (MAD)0.028669678
Skewness0.63271513
Sum55867.468
Variance0.0016988487
MonotonicityNot monotonic
2023-04-19T20:19:37.895483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.31968768 296
 
0.7%
1.292054817 294
 
0.7%
1.291149506 278
 
0.7%
1.290030671 203
 
0.5%
1.294746118 185
 
0.4%
1.293037367 180
 
0.4%
1.338538839 174
 
0.4%
1.294336667 168
 
0.4%
1.320614301 167
 
0.4%
1.370170753 167
 
0.4%
Other values (4530) 39591
94.9%
ValueCountFrequency (%)
1.239336627 4
< 0.1%
1.239415475 1
 
< 0.1%
1.239948547 1
 
< 0.1%
1.242440985 7
< 0.1%
1.242963992 1
 
< 0.1%
1.242974246 2
 
< 0.1%
1.243234247 5
< 0.1%
1.243744865 7
< 0.1%
1.244007259 4
< 0.1%
1.244201733 7
< 0.1%
ValueCountFrequency (%)
1.456436254 11
 
< 0.1%
1.456211379 10
 
< 0.1%
1.456182921 18
< 0.1%
1.456008947 12
 
< 0.1%
1.448200366 4
 
< 0.1%
1.447039421 35
0.1%
1.446856479 14
 
< 0.1%
1.446681447 7
 
< 0.1%
1.446633687 30
0.1%
1.446515198 11
 
< 0.1%

lon
Real number (ℝ)

Distinct4520
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.85135
Minimum103.69561
Maximum103.97119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size651.6 KiB
2023-04-19T20:19:37.950116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum103.69561
5-th percentile103.75158
Q1103.81206
median103.85069
Q3103.89602
95-th percentile103.94561
Maximum103.97119
Range0.2755851
Interquartile range (IQR)0.08396415

Descriptive statistics

Standard deviation0.058796372
Coefficient of variation (CV)0.00056615899
Kurtosis-0.56852466
Mean103.85135
Median Absolute Deviation (MAD)0.0420593
Skewness-0.20412775
Sum4330912.7
Variance0.0034570134
MonotonicityNot monotonic
2023-04-19T20:19:38.002811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103.7526994 296
 
0.7%
103.8055464 294
 
0.7%
103.8045736 278
 
0.7%
103.8171853 203
 
0.5%
103.8068303 185
 
0.4%
103.8377728 180
 
0.4%
103.87008 174
 
0.4%
103.8070201 168
 
0.4%
103.8999986 167
 
0.4%
103.7532909 167
 
0.4%
Other values (4510) 39591
94.9%
ValueCountFrequency (%)
103.6956085 2
 
< 0.1%
103.6958568 2
 
< 0.1%
103.6961287 3
 
< 0.1%
103.6961471 1
 
< 0.1%
103.6962149 11
< 0.1%
103.6963648 10
< 0.1%
103.6963988 3
 
< 0.1%
103.6965264 2
 
< 0.1%
103.6965346 8
< 0.1%
103.696551 19
< 0.1%
ValueCountFrequency (%)
103.9711936 4
< 0.1%
103.9710988 1
 
< 0.1%
103.9710089 4
< 0.1%
103.9706894 3
< 0.1%
103.9706773 1
 
< 0.1%
103.9705715 3
< 0.1%
103.9703292 2
 
< 0.1%
103.9700192 5
< 0.1%
103.9698251 2
 
< 0.1%
103.9695964 1
 
< 0.1%

Interactions

2023-04-19T20:19:34.753652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:29.168738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:29.696543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:30.251300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:30.786226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:31.451273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:32.016870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:32.557660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:33.121147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:33.689610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:34.236240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:34.799525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:29.210037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:29.743760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:30.298355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:30.830181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:31.499633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:32.058870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:32.603063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:33.169148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:33.735351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:34.280922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:34.851986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:29.259324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:29.797214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:30.348992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:30.880253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:31.553558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:32.111837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:32.659433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:33.223084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:33.789016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:34.330002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:34.897437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:29.305585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:29.845121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:30.393990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:30.924413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:31.600658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:32.157497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:32.707767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:33.272533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:33.835663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:34.374864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:34.943997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:29.350720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:29.893802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:30.438149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:31.105541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:31.649622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:32.203394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:32.756545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:33.321326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:33.883295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:34.419922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:34.994369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:29.400138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:29.948018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:30.489446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:31.155298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:31.705078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:32.255985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:32.811497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:33.377520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:33.938082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:34.469644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:35.040427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:29.446979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:29.996299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:30.536830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:31.200067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:31.754471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:32.307942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:32.861862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:33.426363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:33.987319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:34.516134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:35.090585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:29.498350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:30.047968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:30.588692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:31.251758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:31.812290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:32.363092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:32.915955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:33.482105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:34.038700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:34.564866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:35.144006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:29.551866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:30.101784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:30.643158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:31.304112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:31.866519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:32.418071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:32.971335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:33.536041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:34.092073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:34.615696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:35.194329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:29.601409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:30.153662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:30.692792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:31.355125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:31.919332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:32.466293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:33.023857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:33.591128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:34.140502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:34.663034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:35.239043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:29.648369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:30.201350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:30.738300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:31.401298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:31.966288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:32.511239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:33.070752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:33.638605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:34.186953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-19T20:19:34.706969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-19T20:19:38.057313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
area_sqmtotal_priceprice_psmprice_psfcompletion_datepostal_districtpostal_sectorpostal_codedate_idlatlonarea_typeproperty_typesale_typepurchaser_addressplanning_regionplanning_areais_leasehold
area_sqm1.0000.657-0.315-0.315-0.4760.0200.0180.023-0.107-0.023-0.0730.0260.1540.2810.1960.0850.1300.229
total_price0.6571.0000.4340.434-0.174-0.448-0.441-0.4380.046-0.516-0.1500.0000.0590.0760.1010.0900.1580.169
price_psm-0.3150.4341.0001.0000.319-0.613-0.600-0.6020.190-0.653-0.1240.0000.4380.1830.1830.3550.3630.265
price_psf-0.3150.4341.0001.0000.319-0.613-0.600-0.6020.190-0.653-0.1240.0000.4380.1830.1830.3550.3630.265
completion_date-0.476-0.1740.3190.3191.0000.0260.0270.0170.0640.1430.0040.0000.2200.6770.3290.1370.2340.431
postal_district0.020-0.448-0.613-0.6130.0261.0000.9870.986-0.0540.8800.1230.0000.3680.2690.2450.7330.8560.530
postal_sector0.018-0.441-0.600-0.6000.0270.9871.0001.000-0.0510.8770.1480.0030.3630.2680.2530.6800.8740.521
postal_code0.023-0.438-0.602-0.6020.0170.9861.0001.000-0.0550.8750.1480.0020.3630.2660.2520.6790.8730.519
date_id-0.1070.0460.1900.1900.064-0.054-0.051-0.0551.000-0.0210.0990.0000.1860.1090.2320.1210.1760.098
lat-0.023-0.516-0.653-0.6530.1430.8800.8770.875-0.0211.0000.1620.0000.4660.1740.2030.6570.8150.383
lon-0.073-0.150-0.124-0.1240.0040.1230.1480.1480.0990.1621.0000.0000.2870.1440.1360.7350.7850.237
area_type0.0260.0000.0000.0000.0000.0000.0030.0020.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
property_type0.1540.0590.4380.4380.2200.3680.3630.3630.1860.4660.2870.0001.0000.2170.1730.3920.5800.262
sale_type0.2810.0760.1830.1830.6770.2690.2680.2660.1090.1740.1440.0000.2171.0000.3440.1560.3930.421
purchaser_address0.1960.1010.1830.1830.3290.2450.2530.2520.2320.2030.1360.0000.1730.3441.0000.1920.2970.266
planning_region0.0850.0900.3550.3550.1370.7330.6800.6790.1210.6570.7350.0000.3920.1560.1921.0001.0000.358
planning_area0.1300.1580.3630.3630.2340.8560.8740.8730.1760.8150.7850.0000.5800.3930.2971.0001.0000.621
is_leasehold0.2290.1690.2650.2650.4310.5300.5210.5190.0980.3830.2370.0000.2620.4210.2660.3580.6211.000

Missing values

2023-04-19T20:19:35.348558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-19T20:19:35.697535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

project_nameaddressnum_unitsarea_sqmarea_typetotal_pricenett_priceprice_psmprice_psfsale_dateproperty_typetenurecompletion_datesale_typepurchaser_addresspostal_districtpostal_sectorpostal_codeplanning_regionplanning_areadate_idis_leaseholdlatlon
0THE BAYSHORE22 Bayshore Road #03-02188Strata888000-100919372019-02-28Condominium99 Yrs From 31/05/19931996ResalePrivate1646469970East RegionBedok66611.312420103.938628
1KINGSFORD WATERBAY66 Upper Serangoon View #16-121102Strata1280000-1254911662019-02-28Apartment99 Yrs From 03/03/20142018Sub SalePrivate1953533885North East RegionHougang66611.373526103.902708
2THE JOVELL13 Flora Drive #02-11142Strata615000-1464313602019-02-28Condominium99 Yrs From 08/03/20182023New SaleN.A1750506853East RegionPasir Ris66611.358005103.965725
3V ON SHENTON5A Shenton Way #44-121112Strata285568028476802542623622019-02-28Apartment99 Yrs From 29/11/20112017New SaleN.A1668814Central RegionDowntown Core66611.277083103.849181
4THE BEACON130 Cantonment Road #10-041103Strata1570000-1524314162019-02-28Apartment99 Yrs From 05/03/20042008ResalePrivate2889775Central RegionBukit Merah66611.275875103.840481
5WATERBANK AT DAKOTA78 Dakota Crescent #09-16158Strata1080000-1862117302019-02-28Condominium99 Yrs From 07/12/20092013ResalePrivate1439399945Central RegionGeylang66611.306554103.888847
6MARGARET VILLE20 Margaret Drive #15-03185Strata1560650-1836117062019-02-28Apartment99 Yrs From 13/03/20172023New SaleN.A314149312Central RegionQueenstown66611.299517103.802807
7PARK COLONIAL4 Woodleigh Lane #10-12163Strata1359000-2157120042019-02-28Condominium99 Yrs From 11/10/20172023New SaleN.A1335357686Central RegionToa Payoh66611.338682103.869252
8BREEZE BY THE EAST316 Upper East Coast Road #04-031116Strata1530000-1319012252019-02-28CondominiumFreehold2011ResalePrivate1646465520East RegionBedok66601.314138103.937742
9THE TRE VER64 Potong Pasir Avenue 1 #14-24165Strata1127112-1734016112019-02-28Condominium99 Yrs From 27/03/20182023New SaleN.A1335358393Central RegionToa Payoh66611.336988103.864798
project_nameaddressnum_unitsarea_sqmarea_typetotal_pricenett_priceprice_psmprice_psfsale_dateproperty_typetenurecompletion_datesale_typepurchaser_addresspostal_districtpostal_sectorpostal_codeplanning_regionplanning_areadate_idis_leaseholdlatlon
41728QUEENS12 Stirling Road #07-121110Strata1468000-1334512402018-08-01Condominium99 Yrs From 16/02/19982002ResaleHDB314148955Central RegionQueenstown45611.293048103.806248
41729EAST MEADOWS32 Tanah Merah Kechil Road #03-231111Strata1090000-98209122018-08-01Condominium99 Yrs From 02/03/19982002ResaleHDB1646465559East RegionBedok45611.329294103.944215
41730SIMSVILLE10 Geylang East Avenue 2 #03-08191Strata960000-105499802018-08-01Condominium99 Yrs From 01/12/19941998ResalePrivate1438389758Central RegionGeylang45611.316866103.888865
41731THE COAST AT SENTOSA COVE276 Ocean Drive #03-261244Strata4688000-1921317852018-08-01Condominium99 Yrs From 11/04/20062009ResalePrivate4998449Central RegionSouthern Islands45611.246787103.844402
41732CASTLE GREEN485 Yio Chu Kang Road #04-16188Strata810000-92058552018-08-01Condominium99 Yrs From 01/12/19931997ResalePrivate2678787058North East RegionAng Mo Kio45611.385429103.840723
41733PRIVE37 Punggol Field #03-36177Strata828000-107539992018-08-01Executive Condominium99 Yrs From 14/09/20102013ResaleHDB1982828809North East RegionPunggol45611.400676103.904700
41734PARK GREEN12 Rivervale Link #17-211193Strata1470000-76177082018-08-01Executive Condominium99 Yrs From 17/08/20012004ResaleHDB1954545045North East RegionSengkang45611.381216103.900166
41735PRIVE37 Punggol Field #09-35177Strata770000-100009292018-08-01Executive Condominium99 Yrs From 14/09/20102013ResalePrivate1982828809North East RegionPunggol45611.400676103.904700
41736EIGHT COURTYARDS12 Canberra Drive #11-241106Strata1100000-103779642018-08-01Condominium99 Yrs From 20/09/20102014ResalePrivate2776768094North RegionYishun45611.438963103.831365
41737BARTLEY RESIDENCES3A Lorong How Sun #09-19199Strata1420000-1434313332018-08-01Apartment99 Yrs From 29/06/20112015ResalePrivate1953536561North East RegionSerangoon45611.342722103.881873